Optimization of internal burnishing operation for energy efficiency, machined quality, and noise emission

Abstract

Boosting energy efficiency and machining quality are prominent solutions to achieve sustainable production for burnishing operations. In this work, an effective optimization has been performed to enhance the energy efficiency (EFb) and decrease the machining noise (MN) as well as surface roughness (SR) of the internal burnishing operation. The burnishing factors are the spindle speed (S), burnishing feed (f), burnishing depth (D), and the number of rollers (N). The burnishing trails of the hardened material labeled SCr440 have been conducted on a CNC milling machine. The adaptive neuro-based-fuzzy inference system (ANFIS) was used to construct the correlations between the process inputs and burnishing responses. The entropy approach is employed to calculate the weight of each technical objective. The non-dominated sorting particle swarm optimization (NSPSO) is utilized to determine the optimal parameters. A comprehensive model of the production cost is developed to check the effectiveness of the proposed approach. The scientific outcomes revealed that the optimal values of the S, f, D, and N are 1645 RPM, 260 mm/min, 0.08 mm, and 4, respectively. The improvements in the EFb, SR, and MN are 6.98%, 25.00%, and 2.23%, as compared to the initial values. The machining cost is saved by 6.2% at the optimal solution. Moreover, the scientific finding is a potent technical solution to enhance machining performances for the burnishing process of various components having internal holes.